Presented at http://www.zirpins.de/wesoa2010/. More information on WIRE can be found at https://sites.google.com/site/mashtn/research-areas-1/wire

1. Wisdom-Aware Computing: On the Interactive Recommendation of Composition Knowledge Soudip Roy Chowdhury, Carlos Rodríguez, Florian Daniel and Fabio Casati WESOA 2010, December7, 2010, San Francisco, USA

2. What are we talking about? 2 End users Software development Today: simplifying technology and reusing code MDD, BPM, SOA, mashups But there are still two major issues Tools typically don’t speak the language of the user Users typically don’t speak the language of the tools

3. What are we talking about? 3 End users Software development We aim to “teach”users how to develop by showing them how others solved similar problems in the past = By harvesting and recommending community composition knowledge Today: simplifying technology and reusing code MDD, BPM, SOA, mashups But there are still two major issues Tools typically don’t speak the language of the user Users typically don’t speak the language of the tools

4. We want to develop a data processing logic that Fetches news from a news site Adds geo-coordinates to each retrieved item (where possible) Filters the feed according to some keywords Plots the resulting items onto a map Let’s see an example: Yahoo! Pipes

5. We want to develop a data processing logic that Fetches news from a news site Adds geo-coordinates to each retrieved item (where possible) Filters the feed according to some keywords Plots the resulting items onto a map Let’s see an example: Yahoo! Pipes Too complex for end users!

6. We want to develop a data processing logic that Fetches news from a news site Adds geo-coordinates to each retrieved item (where possible) Filters the feed according to some keywords Plots the resulting items onto a map Let’s see an example: Yahoo! Pipes Too complex for end users!

7. So what? Wisdom-aware development = learn from existing mashups/compositions + advise composition knowledge Wisdom = the knowledge of the crowd/community Challenges Identifying the types of advices that can be given and the right times when they can be given Discovering computational knowledge Representing and storing knowledge Searching and retrieving knowledge Reusing knowledge 7

8. O. Greenshpan, T. Milo, N. Polyzotis. Autocompletion for mashups. VLDB’09, pp.538-549.top-k next components from a network of compatible components A.V. Riabov, E. Bouillet, M.D. Feblowitz, Z. Liu, A. Ranganathan. Wishful Search: Interactive Composition of Data Mashups. WWW’08, pp. 775-784.AI planning for goal-driven composition A.H.H. Ngu, M. P. Carlson, Q.Z. Sheng. Semantic-Based Mashup of Composite Applications. IEEE Transactions on Services Computing, vol. 3, no. 1, Jan-Mar 2010. Suggestion of semantically compatible components H. Elmeleegy, A. Ivan, R. Akkiraju, R. Goodwin. MashupAdvisor: A Recommendation Tool for Mashup Development. ICWS’08, pp. 337-344.Semantics + prediction of user goals + AI planning T. Hornung, A. Koschmider, G. Lausen. Recommendation Based Process Modeling Support: Method and User Experience. ER’08, pp. 265-278.Copy/paste of business process parts based on text label similarity State of the art 8

9. O. Greenshpan, T. Milo, N. Polyzotis. Autocompletion for mashups. VLDB’09, pp.538-549.top-k next components from a network of compatible components A.V. Riabov, E. Bouillet, M.D. Feblowitz, Z. Liu, A. Ranganathan. Wishful Search: Interactive Composition of Data Mashups. WWW’08, pp. 775-784.AI planning for goal-driven composition A.H.H. Ngu, M. P. Carlson, Q.Z. Sheng. Semantic-Based Mashup of Composite Applications. IEEE Transactions on Services Computing, vol. 3, no. 1, Jan-Mar 2010. Suggestion of semantically compatible components H. Elmeleegy, A. Ivan, R. Akkiraju, R. Goodwin. MashupAdvisor: A Recommendation Tool for Mashup Development. ICWS’08, pp. 337-344.Semantics + prediction of user goals + AI planning T. Hornung, A. Koschmider, G. Lausen. Recommendation Based Process Modeling Support: Method and User Experience. ER’08, pp. 265-278.Copy/paste of business process parts based on text label similarity State of the art 9 On crowd knowledge (CK) Syntactic similarity

10. State of the art 10 On crowd knowledge (CK) O. Greenshpan, T. Milo, N. Polyzotis. Autocompletion for mashups. VLDB’09, pp.538-549.top-k next components from a network of compatible components A.V. Riabov, E. Bouillet, M.D. Feblowitz, Z. Liu, A. Ranganathan. Wishful Search: Interactive Composition of Data Mashups. WWW’08, pp. 775-784.AI planning for goal-driven composition A.H.H. Ngu, M. P. Carlson, Q.Z. Sheng. Semantic-Based Mashup of Composite Applications. IEEE Transactions on Services Computing, vol. 3, no. 1, Jan-Mar 2010. Suggestion of semantically compatible components H. Elmeleegy, A. Ivan, R. Akkiraju, R. Goodwin. MashupAdvisor: A Recommendation Tool for Mashup Development. ICWS’08, pp. 337-344.Semantics + prediction of user goals + AI planning T. Hornung, A. Koschmider, G. Lausen. Recommendation Based Process Modeling Support: Method and User Experience. ER’08, pp. 265-278.Copy/paste of business process parts based on text label similarity Syntactic similarity Semantic similarity

11. O. Greenshpan, T. Milo, N. Polyzotis. Autocompletion for mashups. VLDB’09, pp.538-549.top-k next components from a network of compatible components A.V. Riabov, E. Bouillet, M.D. Feblowitz, Z. Liu, A. Ranganathan. Wishful Search: Interactive Composition of Data Mashups. WWW’08, pp. 775-784.AI planning for goal-driven composition A.H.H. Ngu, M. P. Carlson, Q.Z. Sheng. Semantic-Based Mashup of Composite Applications. IEEE Transactions on Services Computing, vol. 3, no. 1, Jan-Mar 2010. Suggestion of semantically compatible components H. Elmeleegy, A. Ivan, R. Akkiraju, R. Goodwin. MashupAdvisor: A Recommendation Tool for Mashup Development. ICWS’08, pp. 337-344.Semantics + prediction of user goals + AI planning T. Hornung, A. Koschmider, G. Lausen. Recommendation Based Process Modeling Support: Method and User Experience. ER’08, pp. 265-278.Copy/paste of business process parts based on text label similarity State of the art 11 On crowd knowledge (CK) Syntactic similarity Crowd knowledge Semantic similarity

13. Parameter Value Pattern 14

14. Component Association Pattern 15

15. Connector Pattern 16

16. Data Mapping Pattern 17

17. Complex Pattern 18

18. Discovering CK Mine patterns from existing mashup specifications Techniques: Frequent item set and association rule mining: e.g., for Component Association Patterns and Parameter-Value Patterns Sequential pattern mining: e.g., for Complex Patterns, Component Association Patterns, and Connector Patterns. Graph mining: e.g., for Complex Patterns and Connector Patterns. Link mining: for the discovery of any of the proposed advices, e.g., Data Mapping Patterns Key to success: limited complexity of mashups 19

19. When to give an advice? 20

20. Wisdom-aware dev. environment Extension of mashArt or OMELETTE editor 21

21. Status and future work Currently, work in it’s conceptionphase Understandability/acceptabilitystudy of advice paradigm with end users ongoing (mockups!) Next: Knowledge extraction algorithms Advice repository and query interface Extension of mashup editor 22 End users

22. Conclusion We propose the idea of wisdom-aware computing, i.e., the reuse of community composition knowledge to empower end users If successful: Extend developer base toward non-experts Enable progressive learning and knowledge transfer No explicit semantics provided by anybody People don’t like to tag or annotate Semantics should derive from domain (need for domain-specific mashup platforms!) 23